Publications by authors named "W Randolph Ford"

Agricultural phosphorus (P) losses may result from either recently applied fertilizers or from P accumulated in soil and sediment. While both P sources pose an environmental risk to freshwater systems, differentiating between sources is crucial for identifying and implementing management practices to decrease loss. In this study, laboratory rainfall simulations were completed on runoff boxes and undisturbed soil columns before and after fertilizer application.

View Article and Find Full Text PDF

Breast cancer is a global concern as a leading cause of death for women. Early and precise diagnosis can be vital in handling the disease efficiently. Breast cancer subtyping based on estrogen receptor (ER) status is crucial for determining prognosis and treatment.

View Article and Find Full Text PDF
Article Synopsis
  • The study focuses on detecting multijet signatures from proton-proton collisions at a high energy of 13 TeV, analyzing a dataset totaling 128 fb^{-1}.
  • A special data scouting method is utilized to pick out events with low combined momentum in jets.
  • This research is pioneering in its investigation of electroweak particle production in R-parity violating supersymmetric models, particularly examining hadronically decaying mass-degenerate higgsinos, and it broadens the limits on the existence of R-parity violating top squarks and gluinos.
View Article and Find Full Text PDF

The first search for soft unclustered energy patterns (SUEPs) is performed using an integrated luminosity of 138  fb^{-1} of proton-proton collision data at sqrt[s]=13  TeV, collected in 2016-2018 by the CMS detector at the LHC. Such SUEPs are predicted by hidden valley models with a new, confining force with a large 't Hooft coupling. In events with boosted topologies, selected by high-threshold hadronic triggers, the multiplicity and sphericity of clustered tracks are used to reject the background from standard model quantum chromodynamics.

View Article and Find Full Text PDF

Breast cancer remains a major public health concern, and early detection is crucial for improving survival rates. Metabolomics offers the potential to develop non-invasive screening and diagnostic tools based on metabolic biomarkers. However, the inherent complexity of metabolomic datasets and the high dimensionality of biomarkers complicates the identification of diagnostically relevant features, with multiple studies demonstrating limited consensus on the specific metabolites involved.

View Article and Find Full Text PDF